Tutorials
A number of tutorials are planned as deliverables for this project. Each tutorial focuses on a particular method as it has been implemented in the triangle and/or pyramid frameworks. Each tutorial will incorporate instructions, code, and a small data set. These will be available on github as they are completed and tested, and links to each tutorial will be provided here on the project website. Here are the tutorials currently planned:
Attribute Misclassification Across Scale (in the Pyramid Framework)
Tutorial content designed by Kate Carlson, CU-Boulder. Testing and web implementation by Katie Tyler and George Charisoulis, CU-Boulder.
August 2021
This tutorial demonstrates confusion matrix generation for wetlands classification. The data set covers a portion of Louisiana, with data for two levels of attribute resolution. The coarse resolution shows presence or absence of wetlands, and the fine resolution identifies wetland type (riverine, lacustrine or palustrine). The tutorial has three parts that generate confusion matrices along with Recall, Precision and F1 metrics, import them into a pyramid framework, and perform statistical summaries and visualization of layers within the pyramid. This tutorial is available as a local (offline) version, requiring Anaconda python and Matlab, as well as a GIS platform (an open-source environment such as QGIS, or a commercial platform such as ArcGIS). It is also available in a cloud (online) version using Google Colab to simplify setting environment parameters and downloading software.
LOCAL VERSION
Click here for Instructions (as html)
Click here to access to local version tutorial code and data set
CLOUD (COLAB) VERSION
Click here for Instructions (as html)
Click here to access to cloud version tutorial code and data set
Kernel Density Estimation in the Pyramid Framework
Tutorial designed, tested, and implemented by Cong Ma, USF, August 2024.
This tutorial demonstrates a 3D Pyramid Model (PM) data framework for multi-scale spatial analysis, utilizing advanced visualization techniques to examine point patterns across various scales. Unlike traditional single-scale or discrete-scale analyses, the PM integrates space and scale in a 3D environment, representing continuous spatial pattern variations across multiple scales. 3D visualization techniques (e.g. volume display, isosurfaces, and slicing) allow users to observe nested relations between spatial patterns at different scales and understand the scaling issue and MAUP in spatial analysis. This tutorial is available as a local (offline) version. We are assuming you have basic working knowledge of GIS and MATLAB.
